Applying Learning Theories and Emerging Technologies to Instructional Design

One interesting concept about the way people learn is that people are social, and we learn from others. For example, connectivism combines considerations of existing knowledge, technology, vast amounts of knowledge, and complexity of modern problems to explain how we learn using modern computer technology (Davis, Edmunds, & Kelly-Bateman, 2008). It is surprising how few scientific studies that researchers have conducted on the effects of different online strategies and learning styles, given all of the attention that industry experts have paid to learning styles (Pashler et al., year). Masa and Mayer’s (2006) research is interesting because they found that learning did not improve for those learners who received learning support in their preferred style of learning and that all learners benefitted from effective learning strategies.

The Learning Theories and Instruction course at Walden University has contributed to my understanding of my learning by helping me gain the perspectives of the many different theories that explain the complex process of learning. All of the learning theories work together to explain the ways people learn, and each learning theory has merits (Kerr, 2007). For example, there are different considerations instructional designers should have, according to adult learning theory, that are not thoroughly explained in any other theory, such as providing autonomy and considering their prior knowledge (Conlan, Grabowski, & Smith, 2003). The best learning strategies are those that are effective and motivational at the same time. For example, learners who get practice and feedback learn the skills better, and also gain confidence that they can perform the skills successfully (Artino, 2008). Instructional designers should always provide practice to learners because it is an effective learning strategy, and it also motivates learners.

I have also learned ways to implement emerging technologies into training. Each technology varies in the extent to which it contributes to effective learning (Brown, McCormack, Reeves, Brooks, & Grajek, 2020). Therefore, instructional designers should choose carefully between the options and match the right technology with each given skill in the training. Instructional designers must consider learner motivation in training designs and work to improve motivation purposefully. When learners are self-determined and can identify the benefits of the training, they are more successful at online learning (Artino, 2008).

Instructional designers should consider various learning theories and scientific research to determine which strategies to include in training designs. For example, researchers found that instructors can enhance learning by providing practice tests and providing opportunities to practice throughout training (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013).

Learners are more engaged and motivated when they have real problems to solve because it helps them build confidence that they can successfully apply the skills to their real-world performance (Artino, 2008). Another important lesson for instructional designers is that it is crucial to consider the ethical uses of learner data. It is crucial that instructional designers consider policies for handling data to protect learners (Brown et al., 2020). The most effective learning strategies are also the most practical, such as gaining learners’ attention and providing realistic practice scenarios with feedback. Instructional designers should implement a variety of proven learning strategies from each learning theory as they apply to the training content they are covering.

References

Artino, Jr., A. R. (2008). Promoting Academic Motivation and Self-Regulation: Practical Guidelines for Online Instructors. TechTrends: Linking Research & Practice to Improve Learning, 52(3), 37–45. https://doi-org.ezp.waldenulibrary.org/10.1007/s11528-008-0153-x

Brown, M., McCormack, M., Reeves, J., Brooks, D. C., & Grajek, S. (2020). EDUCAUSE Horizon Report, Teaching and Learning Edition (Louisville, CO: EDUCAUSE, 2020).

Conlan, J., Grabowski, S. & Smith, K. (2003). Adult learning. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology. Retrieved from http://textbook equity.org/Textbooks/Orey_Emergin_Perspectives_Learning.pdf

Davis, C., Edmunds, E., & Kelly-Bateman, V. (2008). Connectivism. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology. Retrieved from http://textbook equity.org/Textbooks/Orey_Emergin_Perspectives_Learning.pdf

Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology. Psychological Science in the Public Interest, 14(1), 4–58. https://doi.org/10.1177/1529100612453266

Kerr, B. (2007). _isms as filter, not blinker (2007, January 1). [Blog post]. Retrieved from http://billkerr2.blogspot.com/2007/01/isms-as-filter-not-blinker.html

Massa, L.J., & Mayer, R.E. (2006). Testing the ATI hypothesis: Should multimedia instruction accommodate verbalizer-visualizer cognitive style? Learning and Individual Differences, 16, 321–336.

Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning Styles: Concepts and Evidence. Psychological Science in the Public Interest, 9(3), 105–119. https://doi-org.ezp.waldenulibrary.org/10.1111/j.1539-6053.2009.01038.